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Solving inverse problems using conditional invertible neural networks

Solving inverse problems using conditional invertible neural networks

31 July 2020
G. A. Padmanabha
N. Zabaras
    AI4CE
ArXivPDFHTML

Papers citing "Solving inverse problems using conditional invertible neural networks"

12 / 12 papers shown
Title
Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
Girnar Goyal
Philipp Holl
Sweta Agrawal
Nils Thuerey
AI4CE
53
0
0
27 Jan 2025
ISR: Invertible Symbolic Regression
ISR: Invertible Symbolic Regression
Tony Tohme
M. J. Khojasteh
Mohsen Sadr
Florian Meyer
Kamal Youcef-Toumi
51
0
0
10 May 2024
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Jiayuan Dong
Christian L. Jacobsen
Mehdi Khalloufi
Maryam Akram
Wanjiao Liu
Karthik Duraisamy
Xun Huan
BDL
59
6
0
08 Apr 2024
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Vijaya Krishna Yalavarthi
Randolf Scholz
Stefan Born
Lars Schmidt-Thieme
AI4TS
37
0
0
09 Feb 2024
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
Nuojin Cheng
Osman Asif Malik
Subhayan De
Stephen Becker
Alireza Doostan
29
9
0
25 May 2023
On Learning the Tail Quantiles of Driving Behavior Distributions via
  Quantile Regression and Flows
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
Jia Yu Tee
Oliver De Candido
Wolfgang Utschick
Philipp Geiger
27
0
0
22 May 2023
VI-DGP: A variational inference method with deep generative prior for
  solving high-dimensional inverse problems
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Yingzhi Xia
Qifeng Liao
Jinglai Li
27
2
0
22 Feb 2023
Maximum Likelihood on the Joint (Data, Condition) Distribution for
  Solving Ill-Posed Problems with Conditional Flow Models
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow Models
John Shelton Hyatt
24
1
0
24 Aug 2022
Conditional Injective Flows for Bayesian Imaging
Conditional Injective Flows for Bayesian Imaging
AmirEhsan Khorashadizadeh
K. Kothari
Leonardo Salsi
Ali Aghababaei Harandi
Maarten V. de Hoop
Ivan Dokmanić
MedIm
31
16
0
15 Apr 2022
Normalizing field flows: Solving forward and inverse stochastic
  differential equations using physics-informed flow models
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
Ling Guo
Hao Wu
Tao Zhou
AI4CE
14
45
0
30 Aug 2021
Invertible Surrogate Models: Joint surrogate modelling and
  reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Friedrich Bethke
R. Pausch
Patrick Stiller
A. Debus
Michael Bussmann
Nico Hoffmann
33
2
0
01 Jun 2021
Convolutional LSTM Network: A Machine Learning Approach for
  Precipitation Nowcasting
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi
Zhourong Chen
Hao Wang
Dit-Yan Yeung
W. Wong
W. Woo
239
7,921
0
13 Jun 2015
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